Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells19205
Missing cells (%)12.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 MiB
Average record size in memory133.0 B

Variable types

Categorical4
Text5
Numeric6

Dataset

Description등록신청사업,영업구분,등록증번호,상호,법인여부,사업장 전화번호,소재지,소재지(도로명),우편번호,등록일자,유효기간만료일자,폐쇄일자,지점설립일자,본점여부,최근수정일자
Author중랑구
URLhttps://data.seoul.go.kr/dataList/OA-10327/S/1/datasetView.do

Alerts

등록일자 is highly overall correlated with 유효기간만료일자 and 3 other fieldsHigh correlation
유효기간만료일자 is highly overall correlated with 등록일자 and 3 other fieldsHigh correlation
폐쇄일자 is highly overall correlated with 등록일자 and 3 other fieldsHigh correlation
지점설립일자 is highly overall correlated with 등록일자 and 3 other fieldsHigh correlation
최근수정일자 is highly overall correlated with 등록일자 and 3 other fieldsHigh correlation
본점여부 is highly imbalanced (93.8%)Imbalance
등록증번호 has 168 (1.7%) missing valuesMissing
사업장 전화번호 has 3471 (34.7%) missing valuesMissing
소재지 has 300 (3.0%) missing valuesMissing
소재지(도로명) has 4783 (47.8%) missing valuesMissing
우편번호 has 5619 (56.2%) missing valuesMissing
유효기간만료일자 has 2025 (20.2%) missing valuesMissing
폐쇄일자 has 1616 (16.2%) missing valuesMissing
지점설립일자 has 1223 (12.2%) missing valuesMissing

Reproduction

Analysis started2024-05-10 22:41:29.859866
Analysis finished2024-05-10 22:42:59.172535
Duration1 minute and 29.31 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
대부업
6124 
대부중개업
3480 
<NA>
 
396

Length

Max length5
Median length3
Mean length3.7356
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row대부업
2nd row대부업
3rd row대부업
4th row대부업
5th row대부업

Common Values

ValueCountFrequency (%)
대부업 6124
61.2%
대부중개업 3480
34.8%
<NA> 396
 
4.0%

Length

2024-05-10T22:42:59.377955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T22:42:59.694683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대부업 6124
61.2%
대부중개업 3480
34.8%
na 396
 
4.0%

영업구분
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
폐업
3744 
<NA>
2858 
타시군구이관
1190 
영업중
856 
유효기간만료
828 
Other values (3)
524 

Length

Max length6
Median length4
Mean length3.5692
Min length2

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row폐업
2nd row폐업
3rd row<NA>
4th row폐업
5th row폐업

Common Values

ValueCountFrequency (%)
폐업 3744
37.4%
<NA> 2858
28.6%
타시군구이관 1190
 
11.9%
영업중 856
 
8.6%
유효기간만료 828
 
8.3%
직권취소 522
 
5.2%
갱신등록불가 1
 
< 0.1%
휴업 1
 
< 0.1%

Length

2024-05-10T22:43:00.475050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T22:43:00.940334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 3744
37.4%
na 2858
28.6%
타시군구이관 1190
 
11.9%
영업중 856
 
8.6%
유효기간만료 828
 
8.3%
직권취소 522
 
5.2%
갱신등록불가 1
 
< 0.1%
휴업 1
 
< 0.1%

등록증번호
Text

MISSING 

Distinct9785
Distinct (%)99.5%
Missing168
Missing (%)1.7%
Memory size156.2 KiB
2024-05-10T22:43:01.464273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length26
Mean length19.560313
Min length4

Characters and Unicode

Total characters192317
Distinct characters76
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9738 ?
Unique (%)99.0%

Sample

1st row2014-서울중구-0047(대부업)
2nd row2016-서울중구-0069(대부업)
3rd row2007-서울특별시-01578(대부업)
4th row2016-서울마포-0025(대부업)
5th row2016-서울강동-00029
ValueCountFrequency (%)
2012-서울특별시 20
 
0.2%
2010-서울 16
 
0.2%
2013-서울특별시 12
 
0.1%
2011-서울특별시 12
 
0.1%
2014-서울특별시 11
 
0.1%
2016-서울특별시 10
 
0.1%
대부업 9
 
0.1%
2015-서울특별시 9
 
0.1%
2018-서울특별시 8
 
0.1%
2017-서울특별시 8
 
0.1%
Other values (9746) 9863
98.8%
2024-05-10T22:43:02.341362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 33971
17.7%
- 19648
 
10.2%
2 15829
 
8.2%
1 11819
 
6.1%
10935
 
5.7%
9807
 
5.1%
8569
 
4.5%
( 8270
 
4.3%
8238
 
4.3%
) 8199
 
4.3%
Other values (66) 57032
29.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 82746
43.0%
Other Letter 73307
38.1%
Dash Punctuation 19648
 
10.2%
Open Punctuation 8270
 
4.3%
Close Punctuation 8199
 
4.3%
Space Separator 147
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
10935
14.9%
9807
13.4%
8569
11.7%
8238
11.2%
7957
10.9%
3574
 
4.9%
2973
 
4.1%
2498
 
3.4%
2491
 
3.4%
2491
 
3.4%
Other values (52) 13774
18.8%
Decimal Number
ValueCountFrequency (%)
0 33971
41.1%
2 15829
19.1%
1 11819
 
14.3%
3 3769
 
4.6%
8 3124
 
3.8%
4 3089
 
3.7%
9 2897
 
3.5%
7 2772
 
3.4%
6 2742
 
3.3%
5 2734
 
3.3%
Dash Punctuation
ValueCountFrequency (%)
- 19648
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8270
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8199
100.0%
Space Separator
ValueCountFrequency (%)
147
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 119010
61.9%
Hangul 73307
38.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
10935
14.9%
9807
13.4%
8569
11.7%
8238
11.2%
7957
10.9%
3574
 
4.9%
2973
 
4.1%
2498
 
3.4%
2491
 
3.4%
2491
 
3.4%
Other values (52) 13774
18.8%
Common
ValueCountFrequency (%)
0 33971
28.5%
- 19648
16.5%
2 15829
13.3%
1 11819
 
9.9%
( 8270
 
6.9%
) 8199
 
6.9%
3 3769
 
3.2%
8 3124
 
2.6%
4 3089
 
2.6%
9 2897
 
2.4%
Other values (4) 8395
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 119010
61.9%
Hangul 73307
38.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 33971
28.5%
- 19648
16.5%
2 15829
13.3%
1 11819
 
9.9%
( 8270
 
6.9%
) 8199
 
6.9%
3 3769
 
3.2%
8 3124
 
2.6%
4 3089
 
2.6%
9 2897
 
2.4%
Other values (4) 8395
 
7.1%
Hangul
ValueCountFrequency (%)
10935
14.9%
9807
13.4%
8569
11.7%
8238
11.2%
7957
10.9%
3574
 
4.9%
2973
 
4.1%
2498
 
3.4%
2491
 
3.4%
2491
 
3.4%
Other values (52) 13774
18.8%

상호
Text

Distinct8669
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-10T22:43:03.119189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length32
Mean length7.7241
Min length1

Characters and Unicode

Total characters77241
Distinct characters770
Distinct categories12 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7584 ?
Unique (%)75.8%

Sample

1st rowBM금융대부
2nd row열린창대부
3rd row미래금융
4th row두리대부
5th row다산대부
ValueCountFrequency (%)
주식회사 871
 
7.3%
대부중개 295
 
2.5%
대부 272
 
2.3%
유한회사 59
 
0.5%
대부업 15
 
0.1%
캐피탈 14
 
0.1%
대부중개업 10
 
0.1%
전당포 9
 
0.1%
9
 
0.1%
미래 9
 
0.1%
Other values (8685) 10336
86.9%
2024-05-10T22:43:04.559343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8527
 
11.0%
8138
 
10.5%
2727
 
3.5%
2285
 
3.0%
2156
 
2.8%
2127
 
2.8%
1902
 
2.5%
1888
 
2.4%
) 1859
 
2.4%
( 1855
 
2.4%
Other values (760) 43777
56.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 67829
87.8%
Uppercase Letter 2280
 
3.0%
Space Separator 1902
 
2.5%
Close Punctuation 1859
 
2.4%
Open Punctuation 1855
 
2.4%
Lowercase Letter 986
 
1.3%
Decimal Number 256
 
0.3%
Other Punctuation 232
 
0.3%
Dash Punctuation 30
 
< 0.1%
Other Symbol 9
 
< 0.1%
Other values (2) 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
8527
 
12.6%
8138
 
12.0%
2727
 
4.0%
2285
 
3.4%
2156
 
3.2%
2127
 
3.1%
1888
 
2.8%
1396
 
2.1%
1111
 
1.6%
1087
 
1.6%
Other values (685) 36387
53.6%
Uppercase Letter
ValueCountFrequency (%)
S 322
14.1%
K 226
 
9.9%
J 185
 
8.1%
C 181
 
7.9%
M 172
 
7.5%
H 135
 
5.9%
B 104
 
4.6%
Y 91
 
4.0%
L 82
 
3.6%
G 77
 
3.4%
Other values (15) 705
30.9%
Lowercase Letter
ValueCountFrequency (%)
n 115
11.7%
e 114
11.6%
o 113
11.5%
a 95
 
9.6%
i 65
 
6.6%
c 55
 
5.6%
t 54
 
5.5%
s 52
 
5.3%
h 36
 
3.7%
m 36
 
3.7%
Other values (15) 251
25.5%
Decimal Number
ValueCountFrequency (%)
1 74
28.9%
2 43
16.8%
4 35
13.7%
9 27
 
10.5%
5 22
 
8.6%
3 19
 
7.4%
6 13
 
5.1%
0 11
 
4.3%
8 8
 
3.1%
7 4
 
1.6%
Other Punctuation
ValueCountFrequency (%)
. 121
52.2%
& 93
40.1%
? 10
 
4.3%
, 3
 
1.3%
* 2
 
0.9%
@ 1
 
0.4%
1
 
0.4%
/ 1
 
0.4%
Space Separator
ValueCountFrequency (%)
1902
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1859
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1855
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 30
100.0%
Other Symbol
ValueCountFrequency (%)
9
100.0%
Letter Number
ValueCountFrequency (%)
2
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 67818
87.8%
Common 6135
 
7.9%
Latin 3268
 
4.2%
Han 20
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
8527
 
12.6%
8138
 
12.0%
2727
 
4.0%
2285
 
3.4%
2156
 
3.2%
2127
 
3.1%
1888
 
2.8%
1396
 
2.1%
1111
 
1.6%
1087
 
1.6%
Other values (668) 36376
53.6%
Latin
ValueCountFrequency (%)
S 322
 
9.9%
K 226
 
6.9%
J 185
 
5.7%
C 181
 
5.5%
M 172
 
5.3%
H 135
 
4.1%
n 115
 
3.5%
e 114
 
3.5%
o 113
 
3.5%
B 104
 
3.2%
Other values (41) 1601
49.0%
Common
ValueCountFrequency (%)
1902
31.0%
) 1859
30.3%
( 1855
30.2%
. 121
 
2.0%
& 93
 
1.5%
1 74
 
1.2%
2 43
 
0.7%
4 35
 
0.6%
- 30
 
0.5%
9 27
 
0.4%
Other values (13) 96
 
1.6%
Han
ValueCountFrequency (%)
2
 
10.0%
2
 
10.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
Other values (8) 8
40.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 67809
87.8%
ASCII 9400
 
12.2%
CJK 20
 
< 0.1%
None 10
 
< 0.1%
Number Forms 2
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
8527
 
12.6%
8138
 
12.0%
2727
 
4.0%
2285
 
3.4%
2156
 
3.2%
2127
 
3.1%
1888
 
2.8%
1396
 
2.1%
1111
 
1.6%
1087
 
1.6%
Other values (667) 36367
53.6%
ASCII
ValueCountFrequency (%)
1902
20.2%
) 1859
19.8%
( 1855
19.7%
S 322
 
3.4%
K 226
 
2.4%
J 185
 
2.0%
C 181
 
1.9%
M 172
 
1.8%
H 135
 
1.4%
. 121
 
1.3%
Other values (62) 2442
26.0%
None
ValueCountFrequency (%)
9
90.0%
1
 
10.0%
CJK
ValueCountFrequency (%)
2
 
10.0%
2
 
10.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
Other values (8) 8
40.0%
Number Forms
ValueCountFrequency (%)
2
100.0%

법인여부
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
개인
7140 
법인
2860 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row개인
2nd row개인
3rd row개인
4th row개인
5th row개인

Common Values

ValueCountFrequency (%)
개인 7140
71.4%
법인 2860
28.6%

Length

2024-05-10T22:43:05.064732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T22:43:05.349211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
개인 7140
71.4%
법인 2860
28.6%
Distinct5785
Distinct (%)88.6%
Missing3471
Missing (%)34.7%
Memory size156.2 KiB
2024-05-10T22:43:05.878161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length40
Mean length10.623219
Min length1

Characters and Unicode

Total characters69359
Distinct characters25
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5186 ?
Unique (%)79.4%

Sample

1st row02-752-2231
2nd row029498945
3rd row024350665
4th row64066337
5th row02-6212-2272
ValueCountFrequency (%)
02 299
 
4.1%
66
 
0.9%
070 37
 
0.5%
1566 11
 
0.1%
1577 8
 
0.1%
010 7
 
0.1%
432 6
 
0.1%
02-501-9533 6
 
0.1%
02-591-0880 6
 
0.1%
434 5
 
0.1%
Other values (6095) 6916
93.9%
2024-05-10T22:43:06.982796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 11291
16.3%
2 10089
14.5%
- 7055
10.2%
5 5889
8.5%
7 5372
7.7%
1 5122
7.4%
6 5064
7.3%
3 4861
7.0%
8 4706
6.8%
4 4645
6.7%
Other values (15) 5265
7.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 61075
88.1%
Dash Punctuation 7055
 
10.2%
Space Separator 930
 
1.3%
Other Punctuation 181
 
0.3%
Close Punctuation 56
 
0.1%
Math Symbol 28
 
< 0.1%
Open Punctuation 19
 
< 0.1%
Other Letter 9
 
< 0.1%
Uppercase Letter 6
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11291
18.5%
2 10089
16.5%
5 5889
9.6%
7 5372
8.8%
1 5122
8.4%
6 5064
8.3%
3 4861
8.0%
8 4706
7.7%
4 4645
7.6%
9 4036
 
6.6%
Other Punctuation
ValueCountFrequency (%)
* 117
64.6%
/ 44
 
24.3%
. 20
 
11.0%
Other Letter
ValueCountFrequency (%)
3
33.3%
3
33.3%
3
33.3%
Uppercase Letter
ValueCountFrequency (%)
K 3
50.0%
T 2
33.3%
S 1
 
16.7%
Math Symbol
ValueCountFrequency (%)
~ 27
96.4%
× 1
 
3.6%
Dash Punctuation
ValueCountFrequency (%)
- 7055
100.0%
Space Separator
ValueCountFrequency (%)
930
100.0%
Close Punctuation
ValueCountFrequency (%)
) 56
100.0%
Open Punctuation
ValueCountFrequency (%)
( 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69344
> 99.9%
Hangul 9
 
< 0.1%
Latin 6
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11291
16.3%
2 10089
14.5%
- 7055
10.2%
5 5889
8.5%
7 5372
7.7%
1 5122
7.4%
6 5064
7.3%
3 4861
7.0%
8 4706
6.8%
4 4645
6.7%
Other values (9) 5250
7.6%
Hangul
ValueCountFrequency (%)
3
33.3%
3
33.3%
3
33.3%
Latin
ValueCountFrequency (%)
K 3
50.0%
T 2
33.3%
S 1
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69349
> 99.9%
Hangul 9
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11291
16.3%
2 10089
14.5%
- 7055
10.2%
5 5889
8.5%
7 5372
7.7%
1 5122
7.4%
6 5064
7.3%
3 4861
7.0%
8 4706
6.8%
4 4645
6.7%
Other values (11) 5255
7.6%
Hangul
ValueCountFrequency (%)
3
33.3%
3
33.3%
3
33.3%
None
ValueCountFrequency (%)
× 1
100.0%

소재지
Text

MISSING 

Distinct8640
Distinct (%)89.1%
Missing300
Missing (%)3.0%
Memory size156.2 KiB
2024-05-10T22:43:07.733910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length64
Median length51
Mean length31.491134
Min length15

Characters and Unicode

Total characters305464
Distinct characters636
Distinct categories13 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7879 ?
Unique (%)81.2%

Sample

1st row서울특별시 중구 다동 92번지 다동빌딩-705
2nd row서울특별시 강동구 암사동 456-27 101호
3rd row서울특별시 강동구 길동 55번지 513 삼익파크맨션-1003
4th row서울특별시 노원구 공릉동 590-1 현대아파트상가 103호
5th row서울특별시 동대문구 이문동 258-40
ValueCountFrequency (%)
서울특별시 9700
 
17.0%
강남구 1629
 
2.8%
서초구 963
 
1.7%
1호 727
 
1.3%
역삼동 726
 
1.3%
송파구 586
 
1.0%
서초동 578
 
1.0%
중구 508
 
0.9%
영등포구 472
 
0.8%
2호 467
 
0.8%
Other values (9482) 40859
71.4%
2024-05-10T22:43:08.854601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
67758
22.2%
1 13395
 
4.4%
12089
 
4.0%
11164
 
3.7%
10466
 
3.4%
9959
 
3.3%
9758
 
3.2%
9713
 
3.2%
9701
 
3.2%
2 8843
 
2.9%
Other values (626) 142618
46.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 166982
54.7%
Space Separator 67758
22.2%
Decimal Number 63390
 
20.8%
Dash Punctuation 5469
 
1.8%
Uppercase Letter 1191
 
0.4%
Other Punctuation 259
 
0.1%
Lowercase Letter 169
 
0.1%
Close Punctuation 108
 
< 0.1%
Open Punctuation 106
 
< 0.1%
Letter Number 27
 
< 0.1%
Other values (3) 5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
12089
 
7.2%
11164
 
6.7%
10466
 
6.3%
9959
 
6.0%
9758
 
5.8%
9713
 
5.8%
9701
 
5.8%
8591
 
5.1%
8438
 
5.1%
7961
 
4.8%
Other values (547) 69142
41.4%
Uppercase Letter
ValueCountFrequency (%)
B 264
22.2%
A 215
18.1%
D 89
 
7.5%
S 83
 
7.0%
I 57
 
4.8%
T 55
 
4.6%
C 52
 
4.4%
K 51
 
4.3%
L 44
 
3.7%
E 39
 
3.3%
Other values (16) 242
20.3%
Lowercase Letter
ValueCountFrequency (%)
e 34
20.1%
i 17
10.1%
n 17
10.1%
r 14
 
8.3%
l 12
 
7.1%
t 10
 
5.9%
c 10
 
5.9%
a 7
 
4.1%
s 6
 
3.6%
b 5
 
3.0%
Other values (13) 37
21.9%
Decimal Number
ValueCountFrequency (%)
1 13395
21.1%
2 8843
14.0%
0 8005
12.6%
3 7009
11.1%
4 5757
9.1%
5 5097
 
8.0%
6 4555
 
7.2%
7 4059
 
6.4%
8 3352
 
5.3%
9 3318
 
5.2%
Other Punctuation
ValueCountFrequency (%)
/ 97
37.5%
, 94
36.3%
. 58
22.4%
3
 
1.2%
@ 3
 
1.2%
# 2
 
0.8%
; 1
 
0.4%
& 1
 
0.4%
Letter Number
ValueCountFrequency (%)
19
70.4%
7
 
25.9%
1
 
3.7%
Close Punctuation
ValueCountFrequency (%)
) 107
99.1%
] 1
 
0.9%
Open Punctuation
ValueCountFrequency (%)
( 105
99.1%
[ 1
 
0.9%
Space Separator
ValueCountFrequency (%)
67758
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5469
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%
Other Number
ValueCountFrequency (%)
½ 1
100.0%
Other Symbol
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 166983
54.7%
Common 137094
44.9%
Latin 1387
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
12089
 
7.2%
11164
 
6.7%
10466
 
6.3%
9959
 
6.0%
9758
 
5.8%
9713
 
5.8%
9701
 
5.8%
8591
 
5.1%
8438
 
5.1%
7961
 
4.8%
Other values (548) 69143
41.4%
Latin
ValueCountFrequency (%)
B 264
19.0%
A 215
15.5%
D 89
 
6.4%
S 83
 
6.0%
I 57
 
4.1%
T 55
 
4.0%
C 52
 
3.7%
K 51
 
3.7%
L 44
 
3.2%
E 39
 
2.8%
Other values (42) 438
31.6%
Common
ValueCountFrequency (%)
67758
49.4%
1 13395
 
9.8%
2 8843
 
6.5%
0 8005
 
5.8%
3 7009
 
5.1%
4 5757
 
4.2%
- 5469
 
4.0%
5 5097
 
3.7%
6 4555
 
3.3%
7 4059
 
3.0%
Other values (16) 7147
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 166982
54.7%
ASCII 138450
45.3%
Number Forms 27
 
< 0.1%
None 5
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
67758
48.9%
1 13395
 
9.7%
2 8843
 
6.4%
0 8005
 
5.8%
3 7009
 
5.1%
4 5757
 
4.2%
- 5469
 
4.0%
5 5097
 
3.7%
6 4555
 
3.3%
7 4059
 
2.9%
Other values (63) 8503
 
6.1%
Hangul
ValueCountFrequency (%)
12089
 
7.2%
11164
 
6.7%
10466
 
6.3%
9959
 
6.0%
9758
 
5.8%
9713
 
5.8%
9701
 
5.8%
8591
 
5.1%
8438
 
5.1%
7961
 
4.8%
Other values (547) 69142
41.4%
Number Forms
ValueCountFrequency (%)
19
70.4%
7
 
25.9%
1
 
3.7%
None
ValueCountFrequency (%)
3
60.0%
½ 1
 
20.0%
1
 
20.0%

소재지(도로명)
Text

MISSING 

Distinct4773
Distinct (%)91.5%
Missing4783
Missing (%)47.8%
Memory size156.2 KiB
2024-05-10T22:43:09.514521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length66
Median length54
Mean length37.193214
Min length21

Characters and Unicode

Total characters194037
Distinct characters614
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4379 ?
Unique (%)83.9%

Sample

1st row서울특별시 중구 다동길 46, 705호 (다동, 다동빌딩)
2nd row서울특별시 중구 서소문로 89-15, 10층 4호 (순화동, 이주빌딩)
3rd row서울특별시 마포구 성산로2길 19, 5층 502호 (성산동, 산.내.들)
4th row서울특별시 강동구 명일로 286, 513동 1003호 (길동, 삼익파크맨션)
5th row서울특별시 강남구 테헤란로87길 29, 엠타워 1004호 (삼성동)
ValueCountFrequency (%)
서울특별시 5217
 
14.1%
강남구 952
 
2.6%
서초구 580
 
1.6%
2층 458
 
1.2%
역삼동 423
 
1.1%
서초동 371
 
1.0%
영등포구 336
 
0.9%
3층 334
 
0.9%
송파구 324
 
0.9%
4층 314
 
0.8%
Other values (6557) 27633
74.8%
2024-05-10T22:43:10.573947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
31744
 
16.4%
1 7442
 
3.8%
, 7128
 
3.7%
6901
 
3.6%
6837
 
3.5%
5742
 
3.0%
5721
 
2.9%
5412
 
2.8%
2 5368
 
2.8%
5277
 
2.7%
Other values (604) 106465
54.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 107924
55.6%
Decimal Number 34604
 
17.8%
Space Separator 31744
 
16.4%
Other Punctuation 7147
 
3.7%
Close Punctuation 5262
 
2.7%
Open Punctuation 5260
 
2.7%
Dash Punctuation 1052
 
0.5%
Uppercase Letter 866
 
0.4%
Lowercase Letter 137
 
0.1%
Letter Number 31
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
6901
 
6.4%
6837
 
6.3%
5742
 
5.3%
5721
 
5.3%
5412
 
5.0%
5277
 
4.9%
5231
 
4.8%
5217
 
4.8%
4288
 
4.0%
2663
 
2.5%
Other values (529) 54635
50.6%
Uppercase Letter
ValueCountFrequency (%)
B 162
18.7%
A 123
14.2%
S 79
9.1%
T 55
 
6.4%
C 54
 
6.2%
K 46
 
5.3%
L 45
 
5.2%
I 43
 
5.0%
E 40
 
4.6%
G 37
 
4.3%
Other values (16) 182
21.0%
Lowercase Letter
ValueCountFrequency (%)
e 24
17.5%
n 16
11.7%
r 14
10.2%
c 11
8.0%
i 11
8.0%
t 9
 
6.6%
o 8
 
5.8%
l 7
 
5.1%
b 6
 
4.4%
w 6
 
4.4%
Other values (12) 25
18.2%
Decimal Number
ValueCountFrequency (%)
1 7442
21.5%
2 5368
15.5%
0 4436
12.8%
3 4104
11.9%
4 2949
 
8.5%
5 2728
 
7.9%
6 2169
 
6.3%
7 2001
 
5.8%
8 1825
 
5.3%
9 1582
 
4.6%
Other Punctuation
ValueCountFrequency (%)
, 7128
99.7%
. 9
 
0.1%
@ 3
 
< 0.1%
/ 3
 
< 0.1%
? 2
 
< 0.1%
1
 
< 0.1%
& 1
 
< 0.1%
Letter Number
ValueCountFrequency (%)
19
61.3%
9
29.0%
3
 
9.7%
Close Punctuation
ValueCountFrequency (%)
) 5261
> 99.9%
] 1
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 5259
> 99.9%
[ 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
31744
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1052
100.0%
Math Symbol
ValueCountFrequency (%)
~ 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 107924
55.6%
Common 85079
43.8%
Latin 1034
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
6901
 
6.4%
6837
 
6.3%
5742
 
5.3%
5721
 
5.3%
5412
 
5.0%
5277
 
4.9%
5231
 
4.8%
5217
 
4.8%
4288
 
4.0%
2663
 
2.5%
Other values (529) 54635
50.6%
Latin
ValueCountFrequency (%)
B 162
15.7%
A 123
 
11.9%
S 79
 
7.6%
T 55
 
5.3%
C 54
 
5.2%
K 46
 
4.4%
L 45
 
4.4%
I 43
 
4.2%
E 40
 
3.9%
G 37
 
3.6%
Other values (41) 350
33.8%
Common
ValueCountFrequency (%)
31744
37.3%
1 7442
 
8.7%
, 7128
 
8.4%
2 5368
 
6.3%
) 5261
 
6.2%
( 5259
 
6.2%
0 4436
 
5.2%
3 4104
 
4.8%
4 2949
 
3.5%
5 2728
 
3.2%
Other values (14) 8660
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 107924
55.6%
ASCII 86081
44.4%
Number Forms 31
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
31744
36.9%
1 7442
 
8.6%
, 7128
 
8.3%
2 5368
 
6.2%
) 5261
 
6.1%
( 5259
 
6.1%
0 4436
 
5.2%
3 4104
 
4.8%
4 2949
 
3.4%
5 2728
 
3.2%
Other values (61) 9662
 
11.2%
Hangul
ValueCountFrequency (%)
6901
 
6.4%
6837
 
6.3%
5742
 
5.3%
5721
 
5.3%
5412
 
5.0%
5277
 
4.9%
5231
 
4.8%
5217
 
4.8%
4288
 
4.0%
2663
 
2.5%
Other values (529) 54635
50.6%
Number Forms
ValueCountFrequency (%)
19
61.3%
9
29.0%
3
 
9.7%
None
ValueCountFrequency (%)
1
100.0%

우편번호
Real number (ℝ)

MISSING 

Distinct1335
Distinct (%)30.5%
Missing5619
Missing (%)56.2%
Infinite0
Infinite (%)0.0%
Mean136244.75
Minimum3163
Maximum410762
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-10T22:43:10.962583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3163
5-th percentile110111
Q1132010
median136074
Q3142868
95-th percentile157010
Maximum410762
Range407599
Interquartile range (IQR)10858

Descriptive statistics

Standard deviation14730.841
Coefficient of variation (CV)0.10812043
Kurtosis35.970851
Mean136244.75
Median Absolute Deviation (MAD)4954
Skewness-0.015327547
Sum5.9688824 × 108
Variance2.1699768 × 108
MonotonicityNot monotonic
2024-05-10T22:43:11.532184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
135080 170
 
1.7%
137070 145
 
1.5%
157010 74
 
0.7%
135010 72
 
0.7%
151050 53
 
0.5%
152050 51
 
0.5%
139200 49
 
0.5%
142070 49
 
0.5%
158070 48
 
0.5%
151015 48
 
0.5%
Other values (1325) 3622
36.2%
(Missing) 5619
56.2%
ValueCountFrequency (%)
3163 1
 
< 0.1%
3182 1
 
< 0.1%
4534 1
 
< 0.1%
4537 1
 
< 0.1%
4538 2
< 0.1%
100011 4
< 0.1%
100012 1
 
< 0.1%
100013 1
 
< 0.1%
100014 2
< 0.1%
100015 2
< 0.1%
ValueCountFrequency (%)
410762 1
 
< 0.1%
158871 1
 
< 0.1%
158860 4
< 0.1%
158859 2
< 0.1%
158857 1
 
< 0.1%
158849 1
 
< 0.1%
158846 1
 
< 0.1%
158845 1
 
< 0.1%
158842 1
 
< 0.1%
158841 1
 
< 0.1%

등록일자
Real number (ℝ)

HIGH CORRELATION 

Distinct3521
Distinct (%)35.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20137270
Minimum20060124
Maximum20240508
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-10T22:43:12.071107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20060124
5-th percentile20070921
Q120091210
median20130319
Q320170918
95-th percentile20230224
Maximum20240508
Range180384
Interquartile range (IQR)79708.25

Descriptive statistics

Standard deviation49022.277
Coefficient of variation (CV)0.0024344053
Kurtosis-0.9297708
Mean20137270
Median Absolute Deviation (MAD)39616.5
Skewness0.44986481
Sum2.013727 × 1011
Variance2.4031836 × 109
MonotonicityNot monotonic
2024-05-10T22:43:12.504284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20080731 32
 
0.3%
20080814 31
 
0.3%
20080818 21
 
0.2%
20080806 20
 
0.2%
20090520 18
 
0.2%
20080926 17
 
0.2%
20081222 16
 
0.2%
20090325 15
 
0.1%
20090402 14
 
0.1%
20080822 14
 
0.1%
Other values (3511) 9802
98.0%
ValueCountFrequency (%)
20060124 1
 
< 0.1%
20060127 1
 
< 0.1%
20060306 1
 
< 0.1%
20060310 2
< 0.1%
20060320 1
 
< 0.1%
20060323 3
< 0.1%
20060324 3
< 0.1%
20060329 2
< 0.1%
20060405 1
 
< 0.1%
20060407 1
 
< 0.1%
ValueCountFrequency (%)
20240508 1
 
< 0.1%
20240507 6
0.1%
20240430 2
 
< 0.1%
20240429 1
 
< 0.1%
20240426 1
 
< 0.1%
20240425 5
0.1%
20240424 2
 
< 0.1%
20240423 1
 
< 0.1%
20240422 4
< 0.1%
20240419 1
 
< 0.1%

유효기간만료일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3308
Distinct (%)41.5%
Missing2025
Missing (%)20.2%
Infinite0
Infinite (%)0.0%
Mean20181595
Minimum20090310
Maximum20270508
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-10T22:43:12.934971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20090310
5-th percentile20120310
Q120141022
median20180110
Q320220128
95-th percentile20260512
Maximum20270508
Range180198
Interquartile range (IQR)79106

Descriptive statistics

Standard deviation44743.312
Coefficient of variation (CV)0.0022170355
Kurtosis-1.000295
Mean20181595
Median Absolute Deviation (MAD)39100
Skewness0.3078712
Sum1.6094822 × 1011
Variance2.001964 × 109
MonotonicityNot monotonic
2024-05-10T22:43:13.482064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20141108 14
 
0.1%
20140711 13
 
0.1%
20110831 13
 
0.1%
20110731 12
 
0.1%
20190720 12
 
0.1%
20140713 11
 
0.1%
20190718 11
 
0.1%
20190624 10
 
0.1%
20140721 10
 
0.1%
20190613 10
 
0.1%
Other values (3298) 7859
78.6%
(Missing) 2025
 
20.2%
ValueCountFrequency (%)
20090310 1
< 0.1%
20091116 1
< 0.1%
20100122 1
< 0.1%
20100125 1
< 0.1%
20100216 1
< 0.1%
20100219 1
< 0.1%
20100323 2
< 0.1%
20100326 1
< 0.1%
20100418 1
< 0.1%
20100427 1
< 0.1%
ValueCountFrequency (%)
20270508 1
 
< 0.1%
20270507 4
< 0.1%
20270506 2
 
< 0.1%
20270430 2
 
< 0.1%
20270429 1
 
< 0.1%
20270426 1
 
< 0.1%
20270425 5
0.1%
20270424 2
 
< 0.1%
20270423 1
 
< 0.1%
20270422 1
 
< 0.1%

폐쇄일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3156
Distinct (%)37.6%
Missing1616
Missing (%)16.2%
Infinite0
Infinite (%)0.0%
Mean20142296
Minimum20081023
Maximum20240510
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-10T22:43:13.981556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20081023
5-th percentile20090916
Q120110411
median20130801
Q320170529
95-th percentile20221027
Maximum20240510
Range159487
Interquartile range (IQR)60118.25

Descriptive statistics

Standard deviation41053.216
Coefficient of variation (CV)0.0020381597
Kurtosis-0.59346643
Mean20142296
Median Absolute Deviation (MAD)29874
Skewness0.67833683
Sum1.6887301 × 1011
Variance1.6853666 × 109
MonotonicityNot monotonic
2024-05-10T22:43:14.697160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20091116 204
 
2.0%
20100927 79
 
0.8%
20160725 23
 
0.2%
20101213 21
 
0.2%
20170124 21
 
0.2%
20101126 15
 
0.1%
20130529 15
 
0.1%
20100308 14
 
0.1%
20110503 14
 
0.1%
20110425 13
 
0.1%
Other values (3146) 7965
79.7%
(Missing) 1616
 
16.2%
ValueCountFrequency (%)
20081023 1
 
< 0.1%
20090125 1
 
< 0.1%
20090128 1
 
< 0.1%
20090211 1
 
< 0.1%
20090305 2
< 0.1%
20090306 1
 
< 0.1%
20090307 1
 
< 0.1%
20090309 3
< 0.1%
20090311 3
< 0.1%
20090312 3
< 0.1%
ValueCountFrequency (%)
20240510 2
< 0.1%
20240509 1
 
< 0.1%
20240503 1
 
< 0.1%
20240502 1
 
< 0.1%
20240501 3
< 0.1%
20240430 3
< 0.1%
20240429 1
 
< 0.1%
20240423 2
< 0.1%
20240422 1
 
< 0.1%
20240418 4
< 0.1%

지점설립일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3560
Distinct (%)40.6%
Missing1223
Missing (%)12.2%
Infinite0
Infinite (%)0.0%
Mean20135779
Minimum19670425
Maximum20240507
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-10T22:43:15.216379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19670425
5-th percentile20070104
Q120100420
median20130514
Q320170620
95-th percentile20220621
Maximum20240507
Range570082
Interquartile range (IQR)70200

Descriptive statistics

Standard deviation47958.13
Coefficient of variation (CV)0.002381737
Kurtosis0.87356587
Mean20135779
Median Absolute Deviation (MAD)30687
Skewness0.050975678
Sum1.7673173 × 1011
Variance2.2999822 × 109
MonotonicityNot monotonic
2024-05-10T22:43:15.694755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20090820 25
 
0.2%
20090520 24
 
0.2%
20090514 19
 
0.2%
20090511 18
 
0.2%
20090507 16
 
0.2%
20090611 15
 
0.1%
20090821 15
 
0.1%
20090617 13
 
0.1%
20090528 13
 
0.1%
20111108 13
 
0.1%
Other values (3550) 8606
86.1%
(Missing) 1223
 
12.2%
ValueCountFrequency (%)
19670425 1
< 0.1%
19770919 1
< 0.1%
19840618 1
< 0.1%
19930107 1
< 0.1%
19930128 1
< 0.1%
19940223 1
< 0.1%
19950711 1
< 0.1%
19951002 1
< 0.1%
19960712 1
< 0.1%
19970302 1
< 0.1%
ValueCountFrequency (%)
20240507 1
 
< 0.1%
20240503 1
 
< 0.1%
20240430 1
 
< 0.1%
20240429 2
 
< 0.1%
20240425 5
0.1%
20240423 1
 
< 0.1%
20240422 3
< 0.1%
20240419 1
 
< 0.1%
20240418 1
 
< 0.1%
20240417 1
 
< 0.1%

본점여부
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
본점
9927 
지점
 
73

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row본점
2nd row본점
3rd row본점
4th row본점
5th row본점

Common Values

ValueCountFrequency (%)
본점 9927
99.3%
지점 73
 
0.7%

Length

2024-05-10T22:43:16.080669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T22:43:16.371884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
본점 9927
99.3%
지점 73
 
0.7%

최근수정일자
Real number (ℝ)

HIGH CORRELATION 

Distinct3146
Distinct (%)31.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20153429
Minimum20090519
Maximum20240510
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-10T22:43:16.715528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20090519
5-th percentile20091118
Q120111017
median20140918
Q320190531
95-th percentile20231012
Maximum20240510
Range149991
Interquartile range (IQR)79514

Descriptive statistics

Standard deviation46118.287
Coefficient of variation (CV)0.0022883593
Kurtosis-1.094155
Mean20153429
Median Absolute Deviation (MAD)30693.5
Skewness0.4274083
Sum2.0153429 × 1011
Variance2.1268964 × 109
MonotonicityNot monotonic
2024-05-10T22:43:17.175657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20091117 81
 
0.8%
20091118 50
 
0.5%
20100927 50
 
0.5%
20090609 46
 
0.5%
20090622 42
 
0.4%
20091116 41
 
0.4%
20130621 37
 
0.4%
20091119 37
 
0.4%
20100330 36
 
0.4%
20160812 29
 
0.3%
Other values (3136) 9551
95.5%
ValueCountFrequency (%)
20090519 1
 
< 0.1%
20090521 3
 
< 0.1%
20090601 3
 
< 0.1%
20090602 3
 
< 0.1%
20090603 9
 
0.1%
20090604 18
 
0.2%
20090605 5
 
0.1%
20090608 1
 
< 0.1%
20090609 46
0.5%
20090610 11
 
0.1%
ValueCountFrequency (%)
20240510 4
< 0.1%
20240509 3
< 0.1%
20240508 4
< 0.1%
20240507 6
0.1%
20240503 4
< 0.1%
20240502 5
0.1%
20240501 6
0.1%
20240430 5
0.1%
20240429 4
< 0.1%
20240426 1
 
< 0.1%

Interactions

2024-05-10T22:42:48.432636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:41:36.341606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:41:41.319458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:41:50.227243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:41:58.988474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:42:08.227176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:42:48.735718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:41:36.654126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:41:41.620180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:41:50.494496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:41:59.349853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:42:11.213383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:42:49.015036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:41:36.949841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:41:41.892161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:41:50.773949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:41:59.693771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:42:17.015249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:42:49.282014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:41:37.245210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:41:42.163807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:41:51.073269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:42:00.012363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:42:22.345311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:42:49.577837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:41:37.535679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:41:42.451941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:41:51.392546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:42:00.407061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:42:27.849418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:42:57.250576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:41:41.019573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:41:49.907486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:41:58.623820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:42:07.847224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:42:42.006453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-10T22:43:17.697382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등록신청사업영업구분법인여부우편번호등록일자유효기간만료일자폐쇄일자지점설립일자본점여부최근수정일자
등록신청사업1.0000.0640.0000.0000.2160.1440.2070.2110.0000.169
영업구분0.0641.0000.1910.0060.5860.5960.2560.3490.0450.479
법인여부0.0000.1911.0000.0700.3330.2710.2570.2670.1890.339
우편번호0.0000.0060.0701.0000.1730.1720.1220.0580.0000.162
등록일자0.2160.5860.3330.1731.0001.0000.9390.7350.0770.939
유효기간만료일자0.1440.5960.2710.1721.0001.0000.9300.7100.0650.840
폐쇄일자0.2070.2560.2570.1220.9390.9301.0000.7310.0630.985
지점설립일자0.2110.3490.2670.0580.7350.7100.7311.0000.1830.703
본점여부0.0000.0450.1890.0000.0770.0650.0630.1831.0000.102
최근수정일자0.1690.4790.3390.1620.9390.8400.9850.7030.1021.000
2024-05-10T22:43:18.020414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등록신청사업법인여부영업구분본점여부
등록신청사업1.0000.0000.0680.000
법인여부0.0001.0000.2040.121
영업구분0.0680.2041.0000.048
본점여부0.0000.1210.0481.000
2024-05-10T22:43:18.294114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우편번호등록일자유효기간만료일자폐쇄일자지점설립일자최근수정일자등록신청사업영업구분법인여부본점여부
우편번호1.0000.0150.0070.0460.0510.0300.0000.0060.0390.000
등록일자0.0151.0000.9970.9610.9240.9660.1660.3460.2550.059
유효기간만료일자0.0070.9971.0000.9640.8980.9670.1100.3540.2070.050
폐쇄일자0.0460.9610.9641.0000.9050.9910.1590.1090.1970.048
지점설립일자0.0510.9240.8980.9051.0000.8960.1420.3260.3310.295
최근수정일자0.0300.9660.9670.9910.8961.0000.1300.2770.2600.078
등록신청사업0.0000.1660.1100.1590.1420.1301.0000.0680.0000.000
영업구분0.0060.3460.3540.1090.3260.2770.0681.0000.2040.048
법인여부0.0390.2550.2070.1970.3310.2600.0000.2041.0000.121
본점여부0.0000.0590.0500.0480.2950.0780.0000.0480.1211.000

Missing values

2024-05-10T22:42:57.697212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-10T22:42:58.322454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-05-10T22:42:58.829739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

등록신청사업영업구분등록증번호상호법인여부사업장 전화번호소재지소재지(도로명)우편번호등록일자유효기간만료일자폐쇄일자지점설립일자본점여부최근수정일자
13455대부업폐업2014-서울중구-0047(대부업)BM금융대부개인<NA>서울특별시 중구 다동 92번지 다동빌딩-705서울특별시 중구 다동길 46, 705호 (다동, 다동빌딩)<NA>20140513201705132016011420140513본점20160119
1384대부업폐업2016-서울중구-0069(대부업)열린창대부개인02-752-2231<NA>서울특별시 중구 서소문로 89-15, 10층 4호 (순화동, 이주빌딩)<NA>20220523202505232023111420160711본점20231114
26096대부업<NA>2007-서울특별시-01578(대부업)미래금융개인<NA>서울특별시 강동구 암사동 456-27 101호<NA><NA>20071227<NA>2010122020071218본점20101220
11278대부업폐업2016-서울마포-0025(대부업)두리대부개인<NA><NA>서울특별시 마포구 성산로2길 19, 5층 502호 (성산동, 산.내.들)<NA>20160705201907052017012420160705본점20170124
11807대부업폐업2016-서울강동-00029다산대부개인<NA>서울특별시 강동구 길동 55번지 513 삼익파크맨션-1003서울특별시 강동구 명일로 286, 513동 1003호 (길동, 삼익파크맨션)<NA>20160425201904252016111520160425본점20161115
28552대부업<NA><NA>크로바캐피탈개인029498945서울특별시 노원구 공릉동 590-1 현대아파트상가 103호<NA><NA>20090202<NA>2010040120090202본점20100401
31177<NA><NA>2006-서울특별시-00018경우대부개인<NA>서울특별시 동대문구 이문동 258-40<NA>13008020060323<NA>2009032420060314본점20090609
31110<NA><NA>2007-서울특별시-00993용성대부개인024350665서울특별시 중랑구 중화동 331-83<NA>13112320070725<NA>20090424<NA>본점20090610
30220<NA><NA>2007-서울특별시-00352미소굿머니개인64066337서울특별시 강남구 개포동 656 시영아파트 30-508<NA>13524020070315<NA>2009051320070308본점20091116
22320대부중개업폐업2010-성북구-00057에이스중개대부개인<NA>서울특별시 성북구 삼선동4가 204번지 3호 고려빌딩 3층<NA>13604420101126201311262012021520101126본점20120215
등록신청사업영업구분등록증번호상호법인여부사업장 전화번호소재지소재지(도로명)우편번호등록일자유효기간만료일자폐쇄일자지점설립일자본점여부최근수정일자
9557대부업폐업2017-서울마포-0008(대부업)한빛월드대부개인1877-7499<NA>서울특별시 마포구 마포대로 86, 522호 (도화동, 창강빌딩)<NA>20170202202002022018011820170202본점20180119
6126대부중개업폐업2018-서울송파-0085(중개업)주식회사 아몬대부중개법인1670-7842서울특별시 송파구 송파동 21번지 송파빌딩-403서울특별시 송파구 백제고분로41길 8, 송파빌딩 403호 (송파동)<NA>20190115202201152020121820160218본점20201218
26423대부업<NA>2008-서울특별시-02048(대부업)조아론개인16881785서울특별시 동대문구 신설동 114-91번지 삼우빌딩 A동 306호<NA>13011020080728<NA>20101115<NA>본점20101115
19588대부업폐업2012-서울중구-0527(대부업)SM대부개인02-2665-8871서울특별시 중구 신당동 432번지 2169호<NA>10083520120601201506012013022520120601본점20130225
30567<NA><NA>2007-서울특별시-01013김영순개인025145816서울특별시 종로구 평창동 89-1 백석빌라 1<NA>11001220070730<NA>2009072120070727본점20090721
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